In current business environments testing analytics is not fully leveraged to drive insight at the lowest level of detail into how to optimize investment strategies, minimize net credit losses and maximize customer relationships to create and maintain a competitive advantage, specifically market testing is a means by which business seek to remedy some of these deficiencies.
The testing process typically comprises a control group and a test group. At the onset of the test, measures are taken to insure that the control group population and the test group populations are the same. A similarity analysis is performed to ensure the same populations and eliminate any differences in the control group versus the test group populations. The same metrics are tracked over the duration of test campaign to indicate a change in test performance of the control group population versus the test group population. The metrics tracked during the test campaign, also referred to herein as the back end test metrics, as well as the front end test metrics are subsequently implemented to determine test performance indicators, such as lift and confidence associated with a specific test metric. The lift calculation provides an indication of the boost (or decline) in the test metric attributable to the test and the confidence calculation provides an insight into the predicted repeatability of the test.
In the current business environment testing and analytics results related to testing tend to be ineffectively leveraged due or otherwise lost due to organizational changes or business entities operating in silo-like environments. In addition to the inability to track performance across multiple business lines/entities, problems persist in testing related to invalid testing, such as statistical insignificance of test and control quantities and the like. Moreover, current inefficient manual test data mining procedures result in the failure to identify lost opportunities.
Additionally, current manual processes do not provide for the requisite efficiency and timeliness needed to determine test performance indicators across any and all segments of the test dataset, such as test participant population dataset or the like. Individual manual calculations must be performed, meaning all too often valuable test information goes undetected due in part by the inability to perform all of the required calculations needed to uncover these performance results.
Thus, a need exists to creating attesting analytics tool that can be leveraged across multiple business lines/entities to accelerate testing insight. In this regard, the desired system should provide for automated collection and storage of data to create a historical database of tests, expected test results, test performance data and testing impacts. Additionally, the desired system should provide for automated data transformation and synchronization to allow for data to be organized for the purpose of data mining and requisite analytic test performance reporting and synchronized to link test participants to front end and back end performance data. Further, the desired system should automate the determination of test performance indicators, such as lift and confidence, across any and all segments of the test dataset and automate the process whereby test results are presented to the user and provide multiple options to the user for generating reports, mining data and the like.